SAMUEL VAITER

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Publications

Preprints

B. Pascal, SV, N. Pustelnik, P. Abry. Automated data-driven selection of the hyperparameters for Total-Variation based texture segmentation. 2020. arXiv:2004.09434.

Q. Bertrand, Q. Klopfenstein, M. Blondel, SV, A. Gramfort, J. Salmon. Implicit differentiation of Lasso-type models for hyperparameter optimization. 2020. arXiv:2002.08943.

N. Keriven, SV. Sparse and Smooth: improved guarantees for Spectral Clustering in the Dynamic Stochastic Block Model. 2020. arXiv:2002.02892.

Q. Klopfenstein, SV. Linear Support Vector Regression with Linear Constraints. 2019. arXiv:1911.02306.

C. Deledalle, N. Papadakis, J. Salmon, SV. Block based refitting in \(\ell_{12}\) sparse regularisation. 2019. arXiv:1910.11186.

M. Massias, SV, A. Gramfort, J. Salmon. Dual Extrapolation for Sparse Generalized Linear Models. 2019. arXiv:1907.05830.

X. Dupuis, SV. The Geometry of Sparse Analysis Regularization. 2019. arXiv:1907.01769.

Articles

A. Barbara, A. Jourani, SV. Maximal Solutions of Sparse Analysis Regularization. J Optim Theory Appl. 180(2):371—396. 2019. arXiv:1703.00192. doi:10.1007/s10957-018-1385-3. (pdf).

SV, G. Peyré, J. Fadili. Model Consistency of Partly Smooth Regularizers. IEEE Trans Inform Theory. 64(3):1725—1737. 2018. arXiv:1405.1004. doi:10.1109/TIT.2017.2713822. (pdf).

A. Chambolle, P. Tan, SV. Accelerated Alternating Descent Methods for Dykstra-like problems. J Math Imaging Vis. 59(3):481—497. 2017. HAL (01346532). doi:10.1007/s10851-017-0724-6. (pdf).

P. Bellec, J. Salmon, SV. A Sharp Oracle Inequality for Graph-Slope. Electron J Statist. 11(2):4851—4870. 2017. arXiv:1706.06977. doi:10.1214/17-EJS1364. (pdf).

SV, C. Deledalle, G. Peyré, J. Fadili, C. Dossal. The Degrees of Freedom of Partly Smooth Regularizers. Ann Inst Stat Math. 69(4):791—832. 2017. arXiv:1404.5557. doi:10.1007/s10463-016-0563-z. (pdf).

C. Deledalle, N. Papadakis, J. Salmon, SV. CLEAR: Covariant LEAst-square Re-fitting with applications to image restoration. SIAM J Imaging Sci. 10(1):243—284. 2017. arXiv:1606.05158. doi:10.1137/16M1080318. (pdf).

SV, M. Golbabaee, J. Fadili, G. Peyré. Model Selection with Low Complexity Priors. Inf. Inference. 4(3):230—287. 2015. arXiv:1307.2342. doi:10.1093/imaiai/iav005. (pdf).

C. Deledalle, SV, J. Fadili, G. Peyré. Stein Unbiased GrAdient estimator of the Risk (SUGAR) for multiple parameter selection. SIAM J Imaging Sci. 7(4):2448—2487. 2014. arXiv:1405.1164. doi:10.1137/140968045. (pdf).

SV, C. Deledalle, G. Peyré, C. Dossal, J. Fadili. Local Behavior of Sparse Analysis Regularization: Applications to Risk Estimation. Appl Comput Harmon Anal. 35(3):433—451. 2013. arXiv:1204.3212. doi:10.1016/j.acha.2012.11.006. (pdf).

SV, G. Peyré, C. Dossal, J. Fadili. Robust Sparse Analysis Regularization. IEEE Trans Inform Theory. 59(4):2001—2016. 2013. arXiv:1109.6222. doi:10.1109/TIT.2012.2233859. (pdf).

Book chapter

SV, G. Peyré, J. Fadili. Low Complexity Regularization of Linear Inverse Problems. 2015. arXiv:1407.1598. doi:10.1007/978-3-319-19749-4. (pdf).

Conference proceedings

C. Deledalle, N. Papadakis, J. Salmon, SV. Refitting solutions promoted by \(\ell_{12}\) sparse analysis regularization with block penalties. SSVM. 2019. arXiv:1903.00741.

Y. Traonmilin, SV. Optimality of 1-norm regularization among weighted 1-norms for sparse recovery: a case study on how to find optimal regularizations. NCMIP. 2018. arXiv:1803.00773. (pdf).

J. Fadili, G. Peyré, SV, C. Deledalle, J. Salmon. Stable Recovery with Analysis Decomposable Priors. SAMPTA. 2013. arXiv:1304.4407. (pdf).

SV, G. Peyré, J. Fadili. Robust Polyhedral Regularization. SAMPTA. 2013. arXiv:1304.6033. (pdf).

C. Deledalle, SV, G. Peyré, J. Fadili, C. Dossal. Unbiased Risk Estimation for Sparse Analysis Regularization. ICIP. 2012. HAL (00662718). doi:10.1109/ICIP.2012.6467544. (pdf).

C. Deledalle, SV, G. Peyré, J. Fadili, C. Dossal. Proximal Splitting Derivatives for Risk Estimation. NCMIP. 2012. HAL (0670213). doi:10.1088/1742-6596/386/1/012003. (pdf).

Workshop communications

M. Massias, SV, A. Gramfort, J. Salmon. Exploiting regularity in sparse Generalized Linear Models. SPARS. 2019. HAL (02288859).

Y. Traonmilin, SV, R. Gribonval. Is the 1-norm the best convex sparse regularization?. iTWIST. 2018. arXiv:1806.08690. (pdf).

C. Deledalle, N. Papadakis, J. Salmon, SV. Characterizing the maximum parameter of the total-variation denoising through the pseudo-inverse of the divergence. SPARS. 2017. arXiv:1612.03080. (pdf).

SV, G. Peyré, J. Fadili. Robustesse au bruit des régularisations polyhédrales. GRETSI. 2013. HAL (00927075). (pdf).

J. Fadili, G. Peyré, SV, C. Deledalle, J. Salmon. Reconstruction Stable par Régularisation Décomposable Analyse. GRETSI. 2013. HAL (00927561). (pdf).

SV, G. Peyré, J. Fadili, C. Deledalle, C. Dossal. The degrees of freedom of the group Lasso for a general design. SPARS. 2013. HAL (00926929). (pdf).

J. Fadili, G. Peyré, SV, C. Deledalle, J. Salmon. Stable Recovery with Analysis Decomposable Priors. SPARS. 2013. HAL (00926727). (pdf).

SV, C. Deledalle, G. Peyré, J. Fadili, C. Dossal. The Degrees of Freedom of the Group Lasso. ICML (sparsity workshop). 2012. arXiv:1205.1481. (pdf).

C. Deledalle, SV, G. Peyré, J. Fadili, C. Dossal. Risk estimation for matrix recovery with spectral regularization. ICML (sparsity workshop). 2012. arXiv:1205.1482. (pdf).

SV, G. Peyré, C. Dossal, J. Fadili. Robust Sparse Analysis Regularization. PICOF. 2012.

Thesis

SV. Low Complexity Regularizations of Inverse Problems. 2014. TEL (01026398). (pdf).